Importing libraries

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In [248]:

Loading the data

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Data Cleaning

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Out[250]:
Region Date Frequency Estimated Unemployment Rate (%) Estimated Employed Estimated Labour Participation Rate (%) Area
0 Andhra Pradesh 31-05-2019 Monthly 3.65 11999139.0 43.24 Rural
1 Andhra Pradesh 30-06-2019 Monthly 3.05 11755881.0 42.05 Rural
2 Andhra Pradesh 31-07-2019 Monthly 3.75 12086707.0 43.50 Rural
3 Andhra Pradesh 31-08-2019 Monthly 3.32 12285693.0 43.97 Rural
4 Andhra Pradesh 30-09-2019 Monthly 5.17 12256762.0 44.68 Rural
In [251]:
Out[251]:
(768, 7)

There are 7 columns in the dataset

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Out[252]:
Index(['Region', ' Date', ' Frequency', ' Estimated Unemployment Rate (%)',
       ' Estimated Employed', ' Estimated Labour Participation Rate (%)',
       'Area'],
      dtype='object')
In [253]:
Out[253]:
Index(['region', 'date', 'frequency', 'estimated_unemployment_rate_(%)',
       'estimated_employed', 'estimated_labour_participation_rate_(%)',
       'area'],
      dtype='object')
In [254]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 7 columns):
 #   Column                                   Non-Null Count  Dtype  
---  ------                                   --------------  -----  
 0   region                                   740 non-null    object 
 1   date                                     740 non-null    object 
 2   frequency                                740 non-null    object 
 3   estimated_unemployment_rate_(%)          740 non-null    float64
 4   estimated_employed                       740 non-null    float64
 5   estimated_labour_participation_rate_(%)  740 non-null    float64
 6   area                                     740 non-null    object 
dtypes: float64(3), object(4)
memory usage: 42.1+ KB
In [255]:
Out[255]:
estimated_unemployment_rate_(%) estimated_employed estimated_labour_participation_rate_(%)
count 740.000000 7.400000e+02 740.000000
mean 11.787946 7.204460e+06 42.630122
std 10.721298 8.087988e+06 8.111094
min 0.000000 4.942000e+04 13.330000
25% 4.657500 1.190404e+06 38.062500
50% 8.350000 4.744178e+06 41.160000
75% 15.887500 1.127549e+07 45.505000
max 76.740000 4.577751e+07 72.570000

Date from object to datetime

In [256]:
Out[256]:
dtype('<M8[ns]')
In [257]:
Out[257]:
region                                     28
date                                       28
frequency                                  28
estimated_unemployment_rate_(%)            28
estimated_employed                         28
estimated_labour_participation_rate_(%)    28
area                                       28
dtype: int64

There are 28 missing values in the dataset

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Out[258]:
(740, 7)
In [259]:
Out[259]:
0

There are no duplicate values in the dataset

In [260]:
Out[260]:
region date frequency estimated_unemployment_rate_(%) estimated_employed estimated_labour_participation_rate_(%) area year
0 Andhra Pradesh 2019-05-31 Monthly 3.65 11999139.0 43.24 Rural 2019
1 Andhra Pradesh 2019-06-30 Monthly 3.05 11755881.0 42.05 Rural 2019
2 Andhra Pradesh 2019-07-31 Monthly 3.75 12086707.0 43.50 Rural 2019
3 Andhra Pradesh 2019-08-31 Monthly 3.32 12285693.0 43.97 Rural 2019
4 Andhra Pradesh 2019-09-30 Monthly 5.17 12256762.0 44.68 Rural 2019
In [261]:
Out[261]:
region date frequency estimated_unemployment_rate_(%) estimated_employed estimated_labour_participation_rate_(%) area year month
0 Andhra Pradesh 2019-05-31 Monthly 3.65 11999139.0 43.24 Rural 2019 5
1 Andhra Pradesh 2019-06-30 Monthly 3.05 11755881.0 42.05 Rural 2019 6
2 Andhra Pradesh 2019-07-31 Monthly 3.75 12086707.0 43.50 Rural 2019 7
3 Andhra Pradesh 2019-08-31 Monthly 3.32 12285693.0 43.97 Rural 2019 8
4 Andhra Pradesh 2019-09-30 Monthly 5.17 12256762.0 44.68 Rural 2019 9
In [262]:
Out[262]:
region date frequency estimated_unemployment_rate_(%) estimated_employed estimated_labour_participation_rate_(%) area year month
0 Andhra Pradesh 2019-05-31 Monthly 3.65 11999139.0 43.24 Rural 2019 May
1 Andhra Pradesh 2019-06-30 Monthly 3.05 11755881.0 42.05 Rural 2019 Jun
2 Andhra Pradesh 2019-07-31 Monthly 3.75 12086707.0 43.50 Rural 2019 Jul
3 Andhra Pradesh 2019-08-31 Monthly 3.32 12285693.0 43.97 Rural 2019 Aug
4 Andhra Pradesh 2019-09-30 Monthly 5.17 12256762.0 44.68 Rural 2019 Sep
In [263]:
Out[263]:
region                                             object
date                                       datetime64[ns]
frequency                                          object
estimated_unemployment_rate_(%)                   float64
estimated_employed                                float64
estimated_labour_participation_rate_(%)           float64
area                                               object
year                                                int32
month                                              object
dtype: object

Univariate - Statistical Non Visual Analysis

In [264]:
In [265]:
In [266]:
********** region **********
count                                                    740
nunique                                                   28
unique     [Andhra Pradesh, Assam, Bihar, Chhattisgarh, D...
Name: region, dtype: object
Value Counts: 
 region
Andhra Pradesh      28
Kerala              28
West Bengal         28
Uttar Pradesh       28
Tripura             28
Telangana           28
Tamil Nadu          28
Rajasthan           28
Punjab              28
Odisha              28
Madhya Pradesh      28
Maharashtra         28
Karnataka           28
Jharkhand           28
Himachal Pradesh    28
Haryana             28
Gujarat             28
Delhi               28
Chhattisgarh        28
Bihar               28
Meghalaya           27
Uttarakhand         27
Assam               26
Puducherry          26
Goa                 24
Jammu & Kashmir     21
Sikkim              17
Chandigarh          12
Name: count, dtype: int64

********** frequency **********
count                      740
nunique                      2
unique     [ Monthly, Monthly]
Name: frequency, dtype: object
Value Counts: 
 frequency
Monthly     381
 Monthly    359
Name: count, dtype: int64

********** area **********
count                 740
nunique                 2
unique     [Rural, Urban]
Name: area, dtype: object
Value Counts: 
 area
Urban    381
Rural    359
Name: count, dtype: int64

********** year **********
count               740
nunique               2
unique     [2019, 2020]
Name: year, dtype: object
Value Counts: 
 year
2019    430
2020    310
Name: count, dtype: int64

********** month **********
count                                                    740
nunique                                                   12
unique     [May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, ...
Name: month, dtype: object
Value Counts: 
 month
May    105
Jun    104
Oct     55
Nov     55
Jul     54
Aug     53
Dec     53
Jan     53
Feb     53
Sep     52
Mar     52
Apr     51
Name: count, dtype: int64
In [267]:
In [268]:
********** estimated_unemployment_rate_(%) **********
min        0.000000
max       76.740000
mean      11.787946
median     8.350000
std       10.721298
Name: estimated_unemployment_rate_(%), dtype: float64

********** estimated_employed **********
min       4.942000e+04
max       4.577751e+07
mean      7.204460e+06
median    4.744178e+06
std       8.087988e+06
Name: estimated_employed, dtype: float64

********** estimated_labour_participation_rate_(%) **********
min       13.330000
max       72.570000
mean      42.630122
median    41.160000
std        8.111094
Name: estimated_labour_participation_rate_(%), dtype: float64

Univariate Visual Analysis

In [269]:
Out[269]:
<Axes: xlabel='estimated_unemployment_rate_(%)', ylabel='Count'>
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Out[270]:
<Axes: xlabel='estimated_employed', ylabel='Count'>
In [271]:
Out[271]:
<Axes: xlabel='estimated_labour_participation_rate_(%)', ylabel='Count'>
In [272]:
0510152025ChandigarhUttarakhandTripuraTamil NaduRajasthanPuducherryMeghalayaMadhya PradeshKarnatakaJammu & KashmirHaryanaGoaChhattisgarhAssam
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhcountregion
In [273]:
Out[273]:
<Axes: xlabel='year', ylabel='count'>
In [274]:
Out[274]:
<Axes: xlabel='month', ylabel='count'>
In [275]:
Out[275]:
<Axes: xlabel='area', ylabel='count'>

Bivariate Visual Analysis

In [276]:
In [277]:
Andhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarh203040506070
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_labour_participation_rate_(%) by regionregionestimated_labour_participation_rate_(%)
In [278]:
Andhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarh020406080
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_unemployment_rate_(%) by regionregionestimated_unemployment_rate_(%)
In [279]:
Andhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarh010M20M30M40M
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_employed by regionregionestimated_employed
In [280]:
Andhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal010M20M30M40MAndhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_employed by regionregionregionestimated_employedyear=2019year=2020
In [281]:
Andhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal020406080Andhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_unemployment_rate_(%) by regionregionregionestimated_unemployment_rate_(%)year=2019year=2020
In [282]:
Andhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal203040506070Andhra PradeshBiharDelhiGujaratHimachal PradeshJharkhandKeralaMaharashtraOdishaPunjabSikkimTelanganaUttar PradeshWest Bengal
regionAndhra PradeshAssamBiharChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest BengalChandigarhestimated_labour_participation_rate_(%) by regionregionregionestimated_labour_participation_rate_(%)year=2019year=2020
In [283]:
In [284]:
Andhra PradeshBiharChhattisgarhGoaHaryanaJammu & KashmirKarnatakaMadhya PradeshMeghalayaPuducherryRajasthanTamil NaduTripuraUttarakhand051015202530Andhra PradeshBiharChhattisgarhGoaHaryanaJammu & KashmirKarnatakaMadhya PradeshMeghalayaPuducherryRajasthanTamil NaduTripuraUttarakhand
regionAndhra PradeshAssamBiharChandigarhChhattisgarhDelhiGoaGujaratHaryanaHimachal PradeshJammu & KashmirJharkhandKarnatakaKeralaMadhya PradeshMaharashtraMeghalayaOdishaPuducherryPunjabRajasthanSikkimTamil NaduTelanganaTripuraUttar PradeshUttarakhandWest Bengalaverage unemployment rate before corona 2019 and after corona 2020 state wiseregionregionestimated_unemployment_rate_(%)year=2019year=2020
In [285]:
In [286]:
In [287]:
Out[287]:
region unemployment_rate_before_corona unemployment_rate_after_corona
0 Andhra Pradesh 4.826875 11.010833
1 Assam 6.420667 6.438182
2 Bihar 13.882500 25.632500
3 Chandigarh 15.822500 16.330000
4 Chhattisgarh 7.346875 11.765000
In [288]:
MeghalayaAssamUttarakhandOdishaGujaratSikkimGoaWest BengalMaharashtraMadhya PradeshAndhra PradeshKarnatakaChhattisgarhTelanganaPunjabKeralaUttar PradeshJammu & KashmirChandigarhRajasthanTamil NaduHimachal PradeshDelhiPuducherryBiharTripuraJharkhandHaryana051015202530
510152025rate_change_in_unemploymentPercentage change in Unemployment rate in each state after coronaregionrate_change_in_unemployment

After analysing the dataset, we can gain insights on how the corona crisis affected the unemployment rate in various states of India. The labour pariticipation rate got decreased during corona. The states most affected in unemployment rate due to corona are Haryana, Jharkhand, Tripura, Bihar and Puducherry whereas Uttarpradesh is the state with most employees.

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